The present invention relates generally to equalization of received signals and more particularly, to reduced complexity approaches to signal equalization to mitigate the effects of intersymbol interference.
There are three main categories of equalizers. Linear equalizers can be thought of as linear filters, which try to collect the energy of the signal that has been spread over the channel response. Linear equalizers may also try to suppress residual interference due to intersymbol interference (ISI), or interference from other signals, by treating the interference as colored noise. Examples of linear receivers include the RAKE and GRAKE receivers and the chip equalizer.
A second category comprises non-linear equalizers that operate over a trellis. The trellis is defined by the state space derived from the channel model. Paths through the trellis represent candidate symbol sequences. These equalizers conduct a breadth first search through the trellis. Examples of non-linear include maximum likelihood sequence estimation (MLSE), truncated MLSE (TMLSE), and decision feedback sequence estimation (DFSE).
A third category is non-linear equalizers that operate over a tree. The tree is also defined by the channel state space. Paths on the tree represent symbol sequences. These equalizers conduct a depth first search, focusing on the most promising symbol sequences first. Examples include the stack algorithm (SA), the M algorithm, and the bucket algorithm.
The use of large signal constellations such as 16, 32 and 64QAM in EDGE, HSPA, LTE and WiMax systems makes equalization more difficult. ISI causes the equalization complexity to grow exponentially with the size of the signal constellation. Thus, there is an interest in designing equalizers with low complexity and good performance for higher order modulation.